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Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings
Author(s) -
Algohary Ahmad,
Viswanath Satish,
Shiradkar Rakesh,
Ghose Soumya,
Pahwa Shivani,
Moses Daniel,
Jambor Ivan,
Shnier Ronald,
Böhm Maret,
Haynes AnneMaree,
Brenner Phillip,
Delprado Warick,
Thompson James,
Pulbrock Marley,
Purysko Andrei S,
Verma Sadhna,
Ponsky Lee,
Stricker Phillip,
Madabhushi Anant
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25983
Subject(s) - medicine , biopsy , radiology , prostate cancer , multiparametric mri , magnetic resonance imaging , effective diffusion coefficient , cancer , nuclear medicine
Background Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). Purpose To evaluate the performance of MRI‐based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. Study Type Retrospective. Subjects Model MRI/TRUS (transperineal grid ultrasound) fusion‐guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. Field Strength/Sequence 3T, T 2 ‐weighted (T 2 w) and diffusion‐weighted (DW) MRI. Assessment A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T 2 w‐MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI‐negative–biopsy‐negative (Group 1, N = 15), MRI‐positive–biopsy‐positive (Group 2, N = 16), MRI‐negative–biopsy‐positive (Group 3, N = 10), and MRI‐positive–biopsy‐negative (Group 4, N = 15). In all, 308 radiomic features (First‐order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T 2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum‐redundancy–maximum‐relevance and used to construct three machine‐learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease. Statistical Tests Wilcoxon rank‐sum tests with P < 0.05 considered statistically significant. Results Seven T 2 w‐based (First‐order Statistics, Haralick, Laws, and Gabor) and three ADC‐based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences ( P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone. Data Conclusion Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI‐TRUS prostate biopsies. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:818–828.